基于多属性决策和聚类的研究前沿排序、分类和进化机制研究

Kai Xiong, Yucheng Dong, Zhaoxia Guo, F. Chiclana, E. Herrera-Viedma
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引用次数: 0

摘要

本研究旨在提出一种多属性决策聚类方法,探讨Web of Science Essential Science Indicators (ESI)数据库中研究前沿的排序、分类及其演化机制。首先,采用文献计量学方法对57个ESI研究前沿的40多篇ESI高被引论文(ESI- hcps)进行特征分析。其次,发现8个具有代表性的指标,得到以下两个问题的答案:(i)谁出版ESI-HCPs以形成研究前沿?(ii)这些esi - hcp在研究前沿的引用来自哪里?接下来,我们对57个ESI研究前沿的排名和集群进行了研究,并基于这些代表性指标揭示了不同集群中研究前沿的演变过程。我们还比较了不同国家在这些研究领域的表现,发现美国和中国在大多数研究领域处于领先地位。然而,在排名、分类和演变方面,两国在不同层次上的表现不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the Ranking, Classifications and Evolution Mechanisms of Research Fronts: A Method Based on Multiattribute Decision Making and Clustering
: This study aims to present a multiattribute decision making and clustering method to explore the ranking, classifications and evolution mechanisms of the research fronts in the Web of Science Essential Science Indicators (ESI) database. First, bibliometrics are used to reveal the characteristics of the 57 ESI research fronts with more than 40 ESI highly cited papers (ESI-HCPs). Second, the 8 representative indicators are discovered to get answers to the following two questions: (i) who publishes ESI-HCPs to form a research front? and (ii) where citations to these ESI-HCPs come from in a research front? Next, we investigate the ranking and clusters among the 57 ESI research fronts and uncover the evolution process of the research fronts in different clusters based on these representative indicators. We also compare the performances of different countries in these research fronts, and find that the USA and China are the leading countries in most research fronts. However, the two countries behave differently at different levels with regard to the rankings, the classifications and the evolution.
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